{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "R.S. says:\n",
    "> Here are the details of the features: Five texture features (energy, entropy, correlation, homogeneity, and contrast) were extracted from T2-w using Haralick descriptors. The features were calculated using the gray-level co-occurrence matrices (GLCM) for each voxel underlying the contoured regions in the image. Voxel-wise texture measures were computed in 28 directions by sliding a window of size 5 × 5 × 5 across the image region enclosing the tumor volume. First, image intensities were rescaled within 0 to 256 range using the entire image. The GLCM was then computed in 3-D using 128 bins in a 5 × 5 × 5 patch centered at each voxel. The texture values for the whole tumor were then summarized using the voxel-wise textures. Nine histogram descriptors (10%, 25%, mean, median, 75%, 90%, stdev, skewness, kurtosis) described above were calculated for each texture feature."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from radiomics import firstorder, shape, glcm\n",
    "import os, sys\n",
    "import numpy as np\n",
    "import SimpleITK as sitk\n",
    "import pandas as pd\n",
    "\n",
    "def normalizedImage(image):\n",
    "    im_arr = sitk.GetArrayFromImage(image).astype(float)    \n",
    "    print(\"Intensity range: \", np.min(im_arr), np.max(im_arr))\n",
    "    im_arr += np.min(im_arr)\n",
    "    im_arr *= 256 / np.max(im_arr)\n",
    "    im = sitk.GetImageFromArray(im_arr.astype(int))\n",
    "    im.CopyInformation(image)\n",
    "    return im\n",
    "\n",
    "def getTexture(image,mask,features):\n",
    "    settings = {\"voxelBased\": True, \"kernelRadius\": 2, \"binCount\": 128}\n",
    "    gf = glcm.RadiomicsGLCM(image, mask, **settings)\n",
    "    for f in features:\n",
    "        gf.enableFeatureByName(f, True)\n",
    "    return gf.execute()\n",
    "    \n",
    "filePrefix = \"../cleanedUp\"\n",
    "firstOrderFeatures = [(\"10\",\"10Percentile\"),       # QG58\n",
    "                      (\"90\",\"90Percentile\"),       # 8DWT\n",
    "                      (\"mean\", \"Mean\"),            # Q4LE\n",
    "                      (\"SD\", \"StandardDeviation\"), # \n",
    "                      (\"Kur\", \"Kurtosis\"),         # IPH6 - 3\n",
    "                      (\"Ske\", \"Skewness\"),         # KE2A\n",
    "                      (\"50\",\"Median\")              # Y12H\n",
    "                     ]\n",
    "shapeFeatures = [(\"ROI_Vol\", \"VoxelVolume\")]\n",
    "                 \n",
    "#                 \"t2_int_25\", \"t2_int_50\", \"t2_int_mean\", \\\n",
    "#\"t2_int_75\",  , , , \"t2_con_10\"]\n",
    "\n",
    "usfFeatures = pd.read_csv(os.path.join(filePrefix,\"Challenge_training_T2w.csv\"),sep=\",\")\n",
    "#####\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "usfFeatureNames = [f for f in usfFeatures.columns if f.startswith(\"t2\")]\n",
    "#usfFeatureNames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "imageFiles = [\"subject0000.nrrd\",\"subject0001.nrrd\"]\n",
    "subjects = [\"0000\", \"0001\",\"0015\",\"0026\",\"0055\"]\n",
    "maskTypes = [\"T_PZ\", \"F_TZ\",\"T_TZ\",\"T_TZ\",\"T_TZ\"]\n",
    "#maskFileSuffixes = [\"subject0000 ROI-1_T_PZ.nrrd\", \"subject0001 ROI-1_F_TZ.nrrd\"]\n",
    "\n",
    "usfValues = []\n",
    "prValues = []\n",
    "subjectValues = []\n",
    "featureTypes = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make feature images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Processing Subject ProstateX-0000\n",
      "Number of voxels in the mask: 1770\n",
      "Mask volume: pyradiomics:1.3275009495019925 / USF: 1.3275\n",
      "Intensity range:  0.0 1118.0\n",
      "Intensity range:  0.0 2267.17094988345\n",
      "Intensity range:  -0.5884242620285078 0.5070356444599216\n",
      "Intensity range:  0.0 0.3632109375\n",
      "Intensity range:  0.0 7.050530384408341\n",
      "Intensity range:  0.0 0.996071058652392\n",
      "\n",
      "Processing Subject ProstateX-0001\n",
      "Number of voxels in the mask: 484\n",
      "Mask volume: pyradiomics:0.363000259637833 / USF: 0.3915\n",
      "Intensity range:  0.0 1118.0\n",
      "Intensity range:  0.0 876.2363436883627\n",
      "Intensity range:  -0.598782534145074 0.34991650998041857\n",
      "Intensity range:  0.0 0.39817768467266695\n",
      "Intensity range:  0.0 6.322168571434607\n",
      "Intensity range:  0.0 0.9980549886260227\n",
      "\n",
      "Processing Subject ProstateX-0015\n",
      "Number of voxels in the mask: 8851\n",
      "Mask volume: pyradiomics:6.638253692924979 / USF: 6.63825\n",
      "Intensity range:  0.0 1153.0\n",
      "Intensity range:  0.0 2335.9382151981226\n",
      "Intensity range:  -0.5792867717482624 0.7046363195045395\n",
      "Intensity range:  0.0 0.330234375\n",
      "Intensity range:  0.0 7.229876141758501\n",
      "Intensity range:  0.0 0.9989940279512236\n",
      "\n",
      "Processing Subject ProstateX-0026\n",
      "Number of voxels in the mask: 13956\n",
      "Mask volume: pyradiomics:10.466989186048508 / USF: 9.28725\n",
      "Intensity range:  0.0 1438.0\n",
      "Intensity range:  0.0 1154.9862470862472\n",
      "Intensity range:  -0.5884964017281384 0.5527027787251501\n",
      "Intensity range:  0.0 0.33809000406796547\n",
      "Intensity range:  0.0 7.115956398277413\n",
      "Intensity range:  0.0 0.9982999981319243\n",
      "\n",
      "Processing Subject ProstateX-0055\n",
      "Number of voxels in the mask: 10364\n",
      "Mask volume: pyradiomics:7.772998764514923 / USF: 7.77525\n",
      "Intensity range:  0.0 1021.0\n",
      "Intensity range:  0.0 821.3334986772487\n",
      "Intensity range:  -0.5690788344221809 0.5242075796505704\n",
      "Intensity range:  0.0 0.3152827380952381\n",
      "Intensity range:  0.0 7.067952131440089\n",
      "Intensity range:  0.0 0.9975442831281333\n"
     ]
    }
   ],
   "source": [
    "# generate texture maps\n",
    "textureNames = [(\"int\", \"original\"),    # original image\n",
    "                (\"con\",\"Contrast\"),     # ACUI\n",
    "                (\"cor\",\"Correlation\"),  # NI2N\n",
    "                (\"ene\",\"JointEnergy\"),  # 8ZQL\n",
    "                (\"ent\",\"JointEntropy\"), # TU9B\n",
    "                (\"hom\",\"Idmn\")          # WF0Z\n",
    "               ]\n",
    "pyrTextureNames = [i for (_,i) in textureNames if i != \"original\"]\n",
    "inputImages = {}\n",
    "inputMasks = {}\n",
    "for (i,s) in enumerate(subjects):\n",
    "    \n",
    "    fullSubjectID = \"ProstateX-\"+s\n",
    "    print(\"\\nProcessing Subject \"+fullSubjectID)\n",
    "\n",
    "    imageFile = os.path.join(filePrefix, \"subject%s.nrrd\" % s)\n",
    "    maskFile = os.path.join(filePrefix, \"subject%s ROI-1_%s.nrrd\" % (s, maskTypes[i]))\n",
    "    image = sitk.ReadImage(imageFile)\n",
    "\n",
    "    mask = sitk.ReadImage(maskFile)\n",
    "    ma_arr = sitk.GetArrayFromImage(mask)\n",
    "    print(\"Number of voxels in the mask: \"+str(np.sum(ma_arr == 1)))\n",
    "\n",
    "    sf = shape.RadiomicsShape(image, mask)\n",
    "    sf.enableFeatureByName('VoxelVolume', True)\n",
    "    print(\"Mask volume: pyradiomics:\"+str(sf.execute()[\"VoxelVolume\"]/1000.)+\" / USF: \"+str(usfFeatures[usfFeatures[\"Patient\"]==fullSubjectID]['ROI_Vol'].values[0]))\n",
    "    \n",
    "    inputMasks[s] = mask\n",
    "    \n",
    "    textureMapsPerSubject = getTexture(normalizedImage(image), mask, pyrTextureNames)\n",
    "    for (_,tn) in textureNames:\n",
    "        if tn == \"original\":\n",
    "            inputImages[s] = [image]\n",
    "        else:\n",
    "            inputImages[s].append(normalizedImage(textureMapsPerSubject[tn]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Original image features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "resultsFrame = pd.DataFrame(columns=[\"Subject\",\"FeatureSource\", \"FeatureName\", \"FeatureValue\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Feature StandardDeviation is deprecated, use with caution!\n",
      "Feature StandardDeviation is deprecated, use with caution!\n",
      "Feature StandardDeviation is deprecated, use with caution!\n",
      "Feature StandardDeviation is deprecated, use with caution!\n",
      "Feature StandardDeviation is deprecated, use with caution!\n",
      "Feature StandardDeviation is deprecated, use with caution!\n",
      "Feature StandardDeviation is deprecated, use with caution!\n",
      "Feature StandardDeviation is deprecated, use with caution!\n",
      "Feature StandardDeviation is deprecated, use with caution!\n",
      "Feature StandardDeviation is deprecated, use with caution!\n",
      "Feature StandardDeviation is deprecated, use with caution!\n",
      "Feature StandardDeviation is deprecated, use with caution!\n",
      "Feature StandardDeviation is deprecated, use with caution!\n",
      "Feature StandardDeviation is deprecated, use with caution!\n",
      "Feature StandardDeviation is deprecated, use with caution!\n",
      "Feature StandardDeviation is deprecated, use with caution!\n",
      "Feature StandardDeviation is deprecated, use with caution!\n",
      "Feature StandardDeviation is deprecated, use with caution!\n",
      "Feature StandardDeviation is deprecated, use with caution!\n",
      "Feature StandardDeviation is deprecated, use with caution!\n",
      "Feature StandardDeviation is deprecated, use with caution!\n",
      "Feature StandardDeviation is deprecated, use with caution!\n",
      "Feature StandardDeviation is deprecated, use with caution!\n",
      "Feature StandardDeviation is deprecated, use with caution!\n",
      "Feature StandardDeviation is deprecated, use with caution!\n",
      "Feature StandardDeviation is deprecated, use with caution!\n",
      "Feature StandardDeviation is deprecated, use with caution!\n",
      "Feature StandardDeviation is deprecated, use with caution!\n",
      "Feature StandardDeviation is deprecated, use with caution!\n",
      "Feature StandardDeviation is deprecated, use with caution!\n"
     ]
    }
   ],
   "source": [
    "subjectsList = []\n",
    "featureSourcesList = []\n",
    "featureNamesList = []\n",
    "featureValuesList = []\n",
    "\n",
    "for (subjectI,subject) in enumerate(subjects):\n",
    "    fullSubjectID = \"ProstateX-\"+subject\n",
    "    \n",
    "    for (imageI,image) in enumerate(inputImages[subject]):\n",
    "\n",
    "        usfPrefix = \"t2_\"+textureNames[imageI][0]\n",
    "        \n",
    "        fof = firstorder.RadiomicsFirstOrder(image, inputMasks[subject])\n",
    "        for (_,prName) in firstOrderFeatures:\n",
    "            fof.enableFeatureByName(prName, True)\n",
    "        prResults = fof.execute()    \n",
    "\n",
    "        for (usfName,prName) in firstOrderFeatures:\n",
    "            usfName = usfPrefix+\"_\"+usfName\n",
    "            usfValue = usfFeatures[usfFeatures[\"Patient\"]==fullSubjectID][usfName].values[0]\n",
    "            \n",
    "            # add USF feature\n",
    "            subjectsList.append(fullSubjectID)\n",
    "            featureSourcesList.append(\"USF\")\n",
    "            featureNamesList.append(usfName)\n",
    "            featureValuesList.append(usfValue)\n",
    "            \n",
    "            # add pyradiomics feature\n",
    "            subjectsList.append(fullSubjectID)\n",
    "            featureSourcesList.append(\"pyradiomics\")\n",
    "            featureNamesList.append(usfName)\n",
    "            featureValuesList.append(prResults[prName])\n",
    "            if prName == \"Kurtosis\":\n",
    "                featureValuesList[-1] = featureValuesList[-1]-3.\n",
    "                \n",
    "resultsFrame[\"Subject\"] = subjectsList\n",
    "resultsFrame[\"FeatureSource\"] = featureSourcesList\n",
    "resultsFrame[\"FeatureName\"] = featureNamesList\n",
    "resultsFrame[\"FeatureValue\"] = featureValuesList"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "resultsFrame.to_csv(\"USF_feature_comparison.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ProstateX-0000\n",
      "2073.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fedorov/.pyenv/versions/3.5.3/lib/python3.5/site-packages/ipykernel_launcher.py:22: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ProstateX-0001\n",
      "794.8\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ProstateX-0015\n",
      "273.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ProstateX-0026\n",
      "4194.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ProstateX-0055\n",
      "3274.7000000000007\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "\n",
    "\n",
    "for (subjectI,subject) in enumerate(subjects):\n",
    "\n",
    "    # Plot\n",
    "    subjectName = \"ProstateX-\"+subject\n",
    "    print(subjectName)\n",
    "    subjectSubset = resultsFrame[resultsFrame[\"Subject\"]==subjectName]\n",
    "    \n",
    "    # subset specific features class\n",
    "    #subjectSubset = subjectSubset[subjectSubset['FeatureName'].str.match('t2_ene_')]\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=(10, 10))\n",
    "    x = subjectSubset[subjectSubset[\"FeatureSource\"]==\"USF\"][\"FeatureValue\"].values\n",
    "    y = subjectSubset[subjectSubset[\"FeatureSource\"]==\"pyradiomics\"][\"FeatureValue\"].values\n",
    "\n",
    "    maxF=np.max([np.max(x),np.max(y)])\n",
    "    print(maxF)\n",
    "    annotations = subjectSubset[resultsFrame[\"FeatureSource\"]==\"USF\"][\"FeatureName\"].values\n",
    "\n",
    "    ax.scatter(x,y)\n",
    "    ax.plot([0,maxF],[0,maxF],c='black',linestyle=':')\n",
    "\n",
    "    plt.title('USF vs pyradiomics - '+subjectName)\n",
    "    plt.xlabel('USF')\n",
    "    plt.ylabel('pyradiomics')\n",
    "\n",
    "\n",
    "    #for (i,annotation) in enumerate(annotations):\n",
    "    #    ax.annotate(annotation,xy=(x[i],y[i]),rotation=45)\n",
    "\n",
    "    plt.show()\n",
    "    \n",
    "    fig.savefig(subjectName+\".png\", bbox_inches='tight')\n",
    "\n",
    "#plt.scatter(usfValues[6:], prValues[6:], c=subjectValues[6:])\n",
    "#plt.title('USF vs pyradiomics')\n",
    "#plt.xlabel('USF')\n",
    "#plt.ylabel('pyradiomics')\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
